I am running the following model (via rstan). It is a binomial mixture model with the two submodels being a Poisson regression for abundance and a binomial regression on the repeated count data conditional on the abundance (marginalized out in this case). My issue is that the model doesn’t fit the data very well, which is often the case with the taxa I work with. I tried added a random grouping effect of site (~4 plots per site, each visited on 5 occasions) to the Poisson GLMM submodel and that didn’t fully fix things so I added a random transect-visit effect to the binomial logistic regression. Usually in BUGS/JAGS this makes the model fit really well (almost perfectly). In this case it doesn’t seem to be fitting as well as expected and the random standard deviation in the logistic regression is correlated with `lp__`

and 'energy__`. I’m wondering if anyone has any recommendations for reparameterizations, different distributions, altered priors, or other suggestions for dealing with the extra-Poisson and/or extra-binomial noise?

This was just a short run of ~600 iterations (400 warmup) but I don’t expect a longer run to really address the core issue.

EDIT: Added plot with energy

```
// Binomial mixture model with covariates
data {
int<lower=0> R; // Number of transects
int<lower=0> T; // Number of temporal replications
int<lower=0> nsites; // Number of sites
int<lower=1> sites[R]; // vector of sites
int<lower=0> y[R, T]; // Counts
vector[R] elev; // Covariate
vector[R] elev2; // Covariate
vector[R] litter; // Covariate
vector[R] stream; // Covariate
vector[R] stream2; // Covariate
matrix[R, T] RH; // Covariate
matrix[R, T] temp; // Covariate
matrix[R, T] temp2; // Covariate
vector[R] gcover; // Covariate
vector[R] gcover2; // Covariate
int<lower=0> K; // Upper bound of population size
}
transformed data {
int<lower=0> max_y[R];
int<lower=0> N_ll;
int foo[R];
for (i in 1:R)
max_y[i] = max(y[i]);
for (i in 1:R) {
foo[i] = K - max_y[i] + 1;
}
N_ll = sum(foo);
}
parameters {
real alpha0;
real alpha1;
real alpha2;
real alpha3;
real alpha4;
real alpha5;
real beta0;
real beta1;
real beta2;
real beta3;
real beta4;
real beta5;
vector[nsites] eps; // Random site effects
real<lower=0,upper=10> sd_eps;
matrix[R, T] delta; // Random transect-visit effects
real<lower=0,upper=10> sd_p;
}
transformed parameters {
vector[R] log_lambda; // Log population size
matrix[R, T] logit_p; // Logit detection probability
for (i in 1:R) {
log_lambda[i] = alpha0 + alpha1 * elev[i] + alpha2 * elev2[i] + alpha3 * litter[i] + alpha4 * stream[i] + alpha5 * stream2[i] + eps[sites[i]];
for (t in 1:T) {
logit_p[i,t] = beta0 + beta1 * RH[i,t] + beta2 * temp[i,t] + beta3 * temp2[i,t] + beta4 * gcover[i] + beta5 * gcover2[i] + delta[i, t];
}
}
}
model {
// Priors
// Improper flat priors are implicitly used on
// alpha0, alpha1, beta0 and beta1.
eps ~ normal(0, sd_eps);
sd_eps ~ uniform(0, 5);
// sd_eps ~ uniform(0, 1); // Implicitly defined
for (i in 1:R) {
for (t in 1:T) {
delta[i,t] ~ normal(0, sd_p);
}
}
sd_p ~ uniform(0, 5);
// Likelihood
for (i in 1:R) {
vector[K - max_y[i] + 1] lp;
for (j in 1:(K - max_y[i] + 1))
lp[j] = poisson_log_lpmf(max_y[i] + j - 1 | log_lambda[i])
+ binomial_logit_lpmf(y[i] | max_y[i] + j - 1, logit_p[i]);
target += log_sum_exp(lp);
}
}
generated quantities {
int<lower=0> N[R]; // Abundance
int totalN;
vector[R] log_lik;
real mean_abundance;
real fit = 0;
real fit_new = 0;
matrix[R, T] p;
matrix[R, T] eval; // Expected values
int y_new[R, T];
matrix[R, T] E;
matrix[R, T] E_new;
vector[K + 1] lp;
for (i in 1:R) {
vector[K - max_y[i] + 1] ll;
for (j in 1:(K - max_y[i] + 1)) {
ll[j] = poisson_log_lpmf(max_y[i] + j - 1 | log_lambda[i])
+ binomial_logit_lpmf(y[i] | max_y[i] + j - 1, logit_p[i]);
}
log_lik[i] = log_sum_exp(ll);
}
for (i in 1:R) {
N[i] = poisson_log_rng(log_lambda[i]);
p[i, 1:T] = inv_logit(logit_p[i, 1:T]);
}
// Bayesian p-value fit
// Initialize N, E and E_new
// N = 0;
for (i in 1:1) {
for(j in 1:T) {
E[i, j] = 0;
E_new[i, j] = 0;
}
}
for (i in 2:R) {
E[i] = E[i - 1];
E_new[i] = E_new[i - 1];
}
for (i in 1:R) {
for (j in 1:T) {
// Assess model fit using Chi-squared discrepancy
// Compute fit statistic E for observed data
eval[i, j] = p[i, j] * N[i];
E[i, j] = square(y[i, j] - eval[i, j]) / (eval[i, j] + 0.5);
// Generate replicate data and
// Compute fit statistic E_new for replicate data
y_new[i, j] = binomial_rng(N[i], p[i, j]);
E_new[i, j] = square(y_new[i, j] - eval[i, j]) / (eval[i, j] + 0.5);
}
}
totalN = sum(N); // Total pop. size across all sites
for (i in 1:R) {
fit = fit + sum(E[i]);
fit_new = fit_new + sum(E_new[i]);
}
mean_abundance = exp(alpha0);
}
```